Discarded Seafloor-objects: A Hidden World Beneath the Waves¶
Introduction to the Sea Floor¶
The ocean's depths are a realm of mystery and discovery, holding wonders that have intrigued scientists and explorers for centuries.
Sadly, the marine environment halso as been a dumping ground for discarded objects for decades. An astonishing variety of discarded objects rest quietly beneath the waves. These forgotten artifacts range from ancient shipwrecks to today's waste; from gigantic swaths of fishing nets to modern-day debris. Some of these objects are pretty hazardous. One example is an estimated 55,000 containers of radioactive waste. These containers were dumped overboard at various Pacific Ocean sites from 1946-1970 (epa., 2022gov).
New technology allows us to investigate these objects and determine their outcomes. We can employ machine learning to determine what might happen to items - will they sink, be buried, or drift? And, if they drift, where will they go? These methods help us determine where hotspots of discarded objects are and help us make more informed decisions and advances in our environmental, ecological and navigational movements.
The Process¶
Our chosen study site was an area off the coast of the Carolinas. We collected a wide range of data to see if we could identify the outcome of the located remnants of history on the ocean floor. This data ranged from shipwrecks and obstructions, artificial reefs, and oyster sanctuaries to currents and waves, sediment type, nightlights fishing data, and more. Once we successfully downloaded our data, we needed to clip it to our area of interest, and finally clean and conform it all to one another. Once this is through and data is wrangled, it is ready for a machine learning model. The output of this model allows us to analyze the state of the ocean floor in our location and infer the ultimate fate of discarded objects.
Goals¶
The goal of this project is to be able to build a model that can predict movement with high confidence of the discarded objects on the sea floor. This is important because movement can cause toxic waste to be released, or destroy the things living on the sea floor, etc.
We created a bounding box GeoDataFrame of the study area to clip our imported data with. We used EPSG:4326. That is shown here:
box = {'geometry': [Polygon([(-77.121369, 36.541466),
(-70.760165, 36.541466),
(-71.511922, 32.087495),
(-79.317663, 31.036502)])]}
bbox_gdf = gpd.GeoDataFrame(box, crs='EPSG:4326')
bbox_gdf.bounds
| minx | miny | maxx | maxy | |
|---|---|---|---|---|
| 0 | -79.317663 | 31.036502 | -70.760165 | 36.541466 |
Our Data¶
AWOIS Wrecks & Electronic Nautical Charts¶
This data comes from the Wrecks and Obstructions Database, managed by the Office of Coast Survey, as well as from Electronic Nautical Charts (ENC's are what the AWOIS database moved towards in 2016). There are over 10,000 wrecks and obstructions managed through this database. We got this data as an excel spreadsheet initially, then decided to use the KMZ (KML) file that was also avialble for consisitency with the Reef Data.
The geodataframe shows our georeferenced locations as well as any potential size and other information about the object. In the following scatter plot are the locations of various wrecks in our region of interest. Our bounding box coordinates run along our x and y axes. It can be a little difficult to gain perspective when seeing these dots without a map in the background, which is why we also created that folium map.
# ENC Wrecks gdf
ENC_gdf = read_kml(ENC_wrecks_kml_pth)
ax = ENC_gdf.plot()
ax.set_xlabel("Longitude")
ax.set_ylabel("Latitude")
ax.set_title('ENC Wrecks')
# Convert ENC gdf to refined df
ENC_df = create_refined_df(ENC_gdf)
ENC_df['description'] = 'ENC Wreck'
Artificial Reefs and Oyster Sanctuaries¶
The North Carolina Division of Marine Fisheries maintains 43 ocean artificial reefs and 25 estuarine reefs. These reefs help promote ecological balance and provide homes for many wild and farmed animals. The materials for these reefs range from sunken vessels and concrete pipes to concrete structures and reef balls- little habitats designed to act as an artificial reef (North Carolina Department of Environmental Quality). We brought this point data into our project as a KML.
A Reef Ball provides habitat for dozens of types of marine life:
image credit: Wikipedia commons
The map below shows point data for the artificial reefs, confirmed wrecks from electric nautical charts, as well as wrecks and obstructions from the AWOIS database in our area of interest. The interactive quality of this map allows the user to zoom in further and further on clusters of objects until identifying information about the point displays. This data is what we can use to see how our inferential data such as currents and sediment are impacting physical ts.
total_df = pd.concat([sc_reef_df, nc_reef_df, AWOIS_df, ENC_df])
# Map our concatonated dataframe
m = folium.Map(location=[32.087495, -71.511922], zoom_start=6)
marker_cluster = MarkerCluster().add_to(m)
for index, row in total_df.iterrows():
folium.Marker(
location=[row.lat, row.lon],
popup=row.description,
icon=folium.Icon(color="black")
).add_to(marker_cluster)
m
